Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\text{IoU} \geq 0.5$ with the box output in the previous step.
Image Credit: Martin Kersner
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 185 | 31.41% |
Semantic Segmentation | 24 | 4.07% |
Real-Time Object Detection | 19 | 3.23% |
Instance Segmentation | 19 | 3.23% |
Pedestrian Detection | 14 | 2.38% |
Image Classification | 11 | 1.87% |
General Classification | 11 | 1.87% |
Classification | 10 | 1.70% |
Autonomous Driving | 9 | 1.53% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |